Plant image analysis plays an important role in agriculture. It is used to record the morphological plant traits regularly and accurately. The plant growth is one of the key traits to be analyzed, which relies on leaf area (i.e., leaf region or plant region) and leaf count. One of the ways to find the leaf count is counting the leaves using segmented plant region. In this paper, a new plant region segmentation scheme is proposed in the orthogonal transform domain based on orthogonal transform coefficients. Initially, an analysis of orthogonal transform coefficients is carried out in terms of the response of orthogonal basis vectors to extract the plant region. After extracting the plant region, the L*a*b and CMYK color spaces are used for noise removal in the segmentation scheme. Finally, the leaves are counted using fine-tuned deep convolutional neural network models. The proposed scheme is experimented on CVPPP benchmark datasets and also tested with the images taken from mobile phone to ensure its reliability and cross-platform applicability. The experiment results on CVPPP benchmark datasets are promising.

中文翻译：

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基于正交变换和深度卷积神经网络的玫瑰花叶植物分割

植物图像分析在农业中起着重要作用。用于定期准确地记录植物的形态特征。植物生长是要分析的关键性状之一，它依赖于叶面积（即叶面积或植物面积）和叶数。查找叶片数的方法之一是使用分段的植物区域对叶片进行计数。本文提出了一种基于正交变换系数的正交变换域植物区域分割新方案。首先，根据正交基向量的响应对正交变换系数进行分析，以提取植物区域。提取植物区域后，L * a * b和CMYK颜色空间用于分割方案中的噪声去除。最后，使用微调的深度卷积神经网络模型对叶子进行计数。该方案在CVPPP基准数据集上进行了实验，并与从手机中获取的图像进行了测试，以确保其可靠性和跨平台适用性。在CVPPP基准数据集上的实验结果很有希望。